288 lines
13 KiB
Python
288 lines
13 KiB
Python
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import os
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import numpy as np
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import pandas as pd
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from scipy.interpolate import interp1d
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from scipy.optimize import curve_fit
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import netCDF4 as nc
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import matplotlib.pyplot as plt
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import seaborn as sns
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import matplotlib.font_manager as fm
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def process_single_file(base_folder_path, i, lat_range=(30, 40)):
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# 构建当前文件夹的路径
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if i < 10:
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folder_name = f"atmPrf_repro2021_2008_00{i}" # 一位数，前面加两个0
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elif i < 100:
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folder_name = f"atmPrf_repro2021_2008_0{i}" # 两位数，前面加一个0
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else:
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folder_name = f"atmPrf_repro2021_2008_{i}" # 三位数，不加0
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folder_path = os.path.join(base_folder_path, folder_name)
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# i should be day
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cache_path_nz = f"{base_folder_path}/{folder_name}_mean_ktemp_Nz.npy"
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cache_path_ptz = f"{base_folder_path}/{folder_name}_mean_ktemp_Ptz.npy"
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if os.path.exists(cache_path_nz) and os.path.exists(cache_path_ptz):
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mean_ktemp_Nz = np.load(cache_path_nz)
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mean_ktemp_Ptz = np.load(cache_path_ptz)
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print(f"Loaded cache for {folder_name}")
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return mean_ktemp_Nz, mean_ktemp_Ptz
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# 检查文件夹是否存在
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if os.path.exists(folder_path):
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dfs = []
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# 遍历文件夹中的文件
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for file_name in os.listdir(folder_path):
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if file_name.endswith('.0390_nc'):
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finfo = os.path.join(folder_path, file_name)
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# print(f"正在处理文件: {finfo}")
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try:
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dataset = nc.Dataset(finfo, 'r')
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# 提取变量数据
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temp = dataset.variables['Temp'][:]
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altitude = dataset.variables['MSL_alt'][:]
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lat = dataset.variables['Lat'][:]
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lon = dataset.variables['Lon'][:]
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# 创建DataFrame
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df = pd.DataFrame({
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'Longitude': lon,
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'Latitude': lat,
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'Altitude': altitude,
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'Temperature': temp
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})
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dataset.close()
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# 剔除高度大于60的行
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df = df[df['Altitude'] <= 60]
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# 对每个文件的数据进行插值
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alt_interp = np.linspace(
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df['Altitude'].min(), df['Altitude'].max(), 3000)
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f_alt = interp1d(
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df['Altitude'], df['Altitude'], kind='linear', fill_value="extrapolate")
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f_lon = interp1d(
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df['Altitude'], df['Longitude'], kind='linear', fill_value="extrapolate")
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f_lat = interp1d(
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df['Altitude'], df['Latitude'], kind='linear', fill_value="extrapolate")
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f_temp = interp1d(
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df['Altitude'], df['Temperature'], kind='linear', fill_value="extrapolate")
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# 计算插值结果
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interpolated_alt = f_alt(alt_interp)
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interpolated_lon = f_lon(alt_interp)
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interpolated_lat = f_lat(alt_interp)
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interpolated_temp = f_temp(alt_interp)
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# 创建插值后的DataFrame
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interpolated_df = pd.DataFrame({
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'Altitude': interpolated_alt,
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'Longitude': interpolated_lon,
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'Latitude': interpolated_lat,
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'Temperature': interpolated_temp
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})
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# 将插值后的DataFrame添加到列表中
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dfs.append(interpolated_df)
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except Exception as e:
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print(f"处理文件 {finfo} 时出错: {e}")
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# 按行拼接所有插值后的DataFrame
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final_df = pd.concat(dfs, axis=0, ignore_index=True)
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# 获取 DataFrame 的长度
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num_rows = len(final_df)
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# 生成一个每3000个数从0到2999的序列并重复
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altitude_values = np.tile(
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np.arange(3000), num_rows // 3000 + 1)[:num_rows]
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# 将生成的值赋给 DataFrame 的 'Altitude' 列
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final_df['Altitude'] = altitude_values
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# 摄氏度换算开尔文
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final_df['Temperature'] = final_df['Temperature'] + 273.15
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lat_begin, lat_end = lat_range
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# 筛选出纬度在30到40度之间的数据
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latitude_filtered_df = final_df[(
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final_df['Latitude'] >= lat_begin) & (final_df['Latitude'] <= lat_end)]
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# 划分经度网格，20°的网格
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lon_min, lon_max = latitude_filtered_df['Longitude'].min(
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), latitude_filtered_df['Longitude'].max()
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lon_bins = np.arange(lon_min, lon_max + 20, 20) # 创建经度网格边界
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# 将数据分配到网格中
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# latitude_filtered_df['Longitude_Grid'] = np.digitize(
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# latitude_filtered_df['Longitude'], lon_bins) - 1
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latitude_filtered_df.loc[:, 'Longitude_Grid'] = np.digitize(
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latitude_filtered_df['Longitude'], lon_bins) - 1
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# 对相同高度的温度取均值，忽略NaN
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altitude_temperature_mean = latitude_filtered_df.groupby(
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'Altitude')['Temperature'].mean().reset_index()
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# 重命名列，使其更具可读性
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altitude_temperature_mean.columns = ['Altitude', 'Mean_Temperature']
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# 定义高度的范围（这里从0到最短段）
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altitude_range = range(0, 3000)
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all_heights_mean_temperature = [] # 用于存储所有高度下的温度均值结果
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for altitude in altitude_range:
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# 筛选出当前高度的所有数据
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altitude_df = latitude_filtered_df[latitude_filtered_df['Altitude'] == altitude]
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# 对Longitude_Grid同一区间的温度取均值
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temperature_mean_by_grid = altitude_df.groupby(
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'Longitude_Grid')['Temperature'].mean().reset_index()
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# 重命名列，使其更具可读性
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temperature_mean_by_grid.columns = [
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'Longitude_Grid', 'Mean_Temperature']
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# 添加高度信息列，方便后续区分不同高度的结果
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temperature_mean_by_grid['Altitude'] = altitude
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# 将当前高度的结果添加到列表中
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all_heights_mean_temperature.append(temperature_mean_by_grid)
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# 将所有高度的结果合并为一个DataFrame
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combined_mean_temperature_df = pd.concat(
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all_heights_mean_temperature, ignore_index=True)
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# 基于Altitude列合并两个DataFrame，只保留能匹配上的行
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merged_df = pd.merge(combined_mean_temperature_df,
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altitude_temperature_mean, on='Altitude', how='inner')
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# 计算差值（减去wn0的扰动）
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merged_df['Temperature_Difference'] = merged_df['Mean_Temperature_x'] - \
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merged_df['Mean_Temperature_y']
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# 按Altitude分组
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grouped = merged_df.groupby('Altitude')
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def single_harmonic(x, A, phi):
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return A * np.sin(2 * np.pi / (18 / k) * x + phi)
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# 初始化存储每个高度的最佳拟合参数、拟合曲线、残差值以及背景温度的字典
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fit_results = {}
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fitted_curves = {}
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residuals = {}
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background_temperatures = {}
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for altitude, group in grouped:
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y_data = group['Temperature_Difference'].values
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x_data = np.arange(len(y_data))
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wn0_data = group['Mean_Temperature_y'].values # 获取同一高度下的wn0数据
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# 检查Temperature_Difference列是否全部为NaN
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if np.all(np.isnan(y_data)):
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fit_results[altitude] = {
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'A': [np.nan] * 5, 'phi': [np.nan] * 5}
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fitted_curves[altitude] = [np.nan * x_data] * 5
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residuals[altitude] = np.nan * x_data
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background_temperatures[altitude] = np.nan * x_data
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else:
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# 替换NaN值为非NaN值的均值
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y_data = np.where(np.isnan(y_data), np.nanmean(y_data), y_data)
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# 初始化存储WN参数和曲线的列表
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wn_params = []
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wn_curves = []
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# 计算wn0（使用Mean_Temperature_y列数据）
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wn0 = wn0_data
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# 对WN1至WN5进行拟合
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for k in range(1, 6):
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# 更新单谐波函数中的k值
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def harmonic_func(
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x, A, phi): return single_harmonic(x, A, phi)
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# 使用curve_fit进行拟合
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popt, pcov = curve_fit(harmonic_func, x_data, y_data, p0=[
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np.nanmax(y_data) - np.nanmin(y_data), 0])
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A_fit, phi_fit = popt
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# 存储拟合结果
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wn_params.append({'A': A_fit, 'phi': phi_fit})
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# 使用拟合参数生成拟合曲线
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WN = harmonic_func(x_data, A_fit, phi_fit)
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wn_curves.append(WN)
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# 计算残差值
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y_data = y_data - WN # 使用残差值作为下一次拟合的y_data
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# 存储结果
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fit_results[altitude] = wn_params
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fitted_curves[altitude] = wn_curves
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residuals[altitude] = y_data
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# 计算同一高度下的背景温度（wn0 + wn1 + wn2 + wn3 + wn4 + wn5）
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wn_sum = np.sum([wn0] + wn_curves, axis=0)
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background_temperatures[altitude] = wn_sum
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# 将每个字典转换成一个 DataFrame
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df = pd.DataFrame(residuals)
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# 使用前向填充（用上一个有效值填充 NaN）
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df.ffill(axis=1, inplace=True)
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# 初始化一个新的字典来保存处理结果
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result = {}
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# 定义滤波范围
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lambda_low = 2 # 2 km
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lambda_high = 15 # 15 km
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f_low = 2 * np.pi / lambda_high
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f_high = 2 * np.pi / lambda_low
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# 循环处理df的每一行（每个高度）
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for idx, residuals_array in df.iterrows():
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# 提取有效值
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valid_values = np.ma.masked_array(
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residuals_array, np.isnan(residuals_array))
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compressed_values = valid_values.compressed() # 去除NaN值后的数组
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N = len(compressed_values) # 有效值的数量
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# 如果有效值为空（即所有值都是NaN），则将结果设置为NaN
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if N == 0:
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result[idx] = np.full_like(residuals_array, np.nan)
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else:
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# 时间序列和频率
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dt = 0.02 # 假设的时间间隔
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n = np.arange(N)
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f = n / (N * dt)
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# 傅里叶变换
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y = np.fft.fft(compressed_values)
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# 频率滤波
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yy = y.copy()
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freq_filter = (f >= f_low) & (f <= f_high)
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yy[~freq_filter] = 0
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# 逆傅里叶变换
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perturbation_after = np.real(np.fft.ifft(yy))
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# 将处理结果插回到result字典中
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result[idx] = perturbation_after
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# 处理背景温度和扰动温度数据格式
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heights = list(background_temperatures.keys())
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data_length = len(next(iter(background_temperatures.values())))
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background_matrix = np.zeros((data_length, len(heights)))
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for idx, height in enumerate(heights):
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background_matrix[:, idx] = background_temperatures[height]
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heights = list(result.keys())
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data_length = len(next(iter(result.values())))
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perturbation_matrix = np.zeros((data_length, len(heights)))
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for idx, height in enumerate(heights):
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perturbation_matrix[:, idx] = result[height]
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perturbation_matrix = perturbation_matrix.T
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# 计算 Brunt-Väisälä 频率和势能
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heights_for_calc = np.linspace(0, 60, 3000) * 1000
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def brunt_vaisala_frequency(g, BT_z, c_p, heights):
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# 计算位温随高度的变化率
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dBT_z_dz = np.gradient(BT_z, heights)
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# 计算 Brunt-Väisälä 频率，根号内取绝对值
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frequency_squared = (g / BT_z) * ((g / c_p) + dBT_z_dz)
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frequency = np.sqrt(np.abs(frequency_squared))
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return frequency
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# 计算势能
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def calculate_gravitational_potential_energy(g, BT_z, N_z, PT_z):
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# 计算势能
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return 0.5 * ((g / N_z) ** 2) * ((PT_z / BT_z) ** 2)
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g = 9.81 # 重力加速度
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c_p = 1004.5 # 比热容
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N_z_matrix = []
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PT_z_matrix = []
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for i in range(background_matrix.shape[0]):
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BT_z = np.array(background_matrix[i])
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PT_z = np.array(perturbation_matrix[i])
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N_z = brunt_vaisala_frequency(g, BT_z, c_p, heights_for_calc)
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PW = calculate_gravitational_potential_energy(g, BT_z, N_z, PT_z)
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N_z_matrix.append(N_z)
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PT_z_matrix.append(PW)
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ktemp_Nz = np.vstack(N_z_matrix)
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ktemp_Ptz = np.vstack(PT_z_matrix)
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mean_ktemp_Nz = np.mean(ktemp_Nz, axis=0)
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mean_ktemp_Ptz = np.mean(ktemp_Ptz, axis=0)
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# save to cache
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np.save(cache_path_nz, mean_ktemp_Nz)
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np.save(cache_path_ptz, mean_ktemp_Ptz)
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return mean_ktemp_Nz, mean_ktemp_Ptz
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else:
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print(f"文件夹 {folder_path} 不存在。")
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return None, None
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